Note

• Datatype: 4-bit GPTQ quantised Model
• Model Source

Introduction

The Qwen-VL-Chat Model is a state-of-the-art Large Vision Language Model (LVLM) developed by Alibaba Cloud. It is designed to understand and generate human-like text based on both visual and textual inputs. It builds upon the capabilities of the Qwen-VL series, integrating advanced vision and language processing for various applications.

Qwen-VL-Chat LMM

Qwen-VL-Chat is a variant within the Qwen-VL series, which also includes the base Qwen-VL model. These models are designed to process and integrate information from both images and text. Qwen-VL-Chat specifically enhances chat-based applications, enabling sophisticated dialogue systems that can interpret visual content alongside textual data.

Run Qwen-VL-Chat with an API

Running the API with Clarifai's Python SDK

You can run the Qwen-VL-Chat Model API using Clarifai’s Python SDK.

Export your PAT as an environment variable. Then, import and initialize the API Client.

Find your PAT in your security settings.

export CLARIFAI_PAT={your personal access token}

Predict via Image URL

from clarifai.client.model import Model
from clarifai.client.input import Inputs

prompt = "What time of day is it?"
image_url = "https://samples.clarifai.com/metro-north.jpg"
inference_params = dict(temperature=0.2, max_tokens=100)

model_prediction = Model("https://clarifai.com/qwen/qwen-VL/models/qwen-VL-Chat").predict(inputs = [Inputs.get_multimodal_input(input_id="",image_url=image_url, raw_text=prompt)],inference_params=inference_params)

print(model_prediction.outputs[0].data.text.raw)

You can also run Qwen-VL-Chat API using other Clarifai Client Libraries like Java, cURL, NodeJS, PHP, etc here.

Use Cases

Qwen-VL-Chat is ideal for a variety of applications:

• Customer Support: Can assist customers by answering queries that involve understanding product images alongside textual descriptions.
• Educational Tools: Supports interactive learning by answering questions from educational content that includes diagrams and text.
• Content Creation: Aids in generating descriptive content for images, suitable for journalism and social media.
• Accessible Technology: Enhances tools for the visually impaired by describing images and interpreting visual data in text form.

Evaluation and Benchmark Results

Qwen-VL-Chat was evaluated across multiple benchmarks:

• Standard Benchmarks:
• Zero-shot Captioning: Demonstrates strong capability in generating image descriptions without prior specific training on the dataset.
• General VQA: Effective in answering general questions about images regarding judgment, colors, numbers, and categories.
• Text-based VQA: Specializes in recognizing and answering questions about text within images.
• Referring Expression Comprehension: Excellently localizes and describes objects within images based on textual descriptions.
• TouchStone Benchmark:
• Evaluates text-image dialogue capabilities and alignment with human-like conversational responses.
• Covers over 300 images, 800+ questions across 27 diverse categories.
• Results show Qwen-VL-Chat's superior performance in creating coherent and contextually appropriate responses in both English and Chinese.

The evaluation table highlights Qwen-VL-Chat's competitive performance across different tasks compared to other LVLM models. In zero-shot captioning, it achieves comparable results to previous SOTA models such as BLIP-2 and InstructBLIP. 

In general VQA and text-oriented VQA, Qwen-VL-Chat performs competitively, demonstrating its robustness in understanding both textual and visual content. Additionally, in referring expression comprehension tasks, it shows promising results, although slightly lower compared to some previous SOTA models.

Advantages

• Multimodal Integration: Seamlessly integrates text and image data, providing a holistic understanding that is crucial for various AI-driven applications.
• Multi-language Capability: Supports dialogues in multiple languages, making it versatile and globally applicable.
• High Resolution Capability: Ability to process high-resolution images allows for finer detail understanding and higher accuracy in tasks requiring detailed visual comprehension.

Limitations

• Data Dependency: Performance heavily relies on the diversity and quality of the training data.
• Computational Requirements: High-resolution image processing requires significant computational power, potentially limiting deployment in resource-constrained environments.
• Language Limitations: While it supports multiple languages, the quality of non-English language processing might vary depending on the specific language and available data